Probabilistic XML Data Research Articles

Keyword Search over Probabilistic XML Documents Based on Node Classification

This paper describes a keyword search measure on probabilistic XML data based on ELM (extreme learning machine). We use this method to carry out keyword search on probabilistic XML data. A probabilistic XML document differs from a traditional XML document to realize keyword search in the consideration of possible world semantics. A probabilistic XML document can be seen as a set of nodes consisting of ordinary nodes and distributional nodes. ELM has good performance in text classification applications. As the typical semistructured data; the label of XML data possesses the function of definition itself. Label and context of the node can be seen as the text data of this node. ELM offers significant advantages such as fast learning speed, ease of implementation, and effective node classification. Set intersection can compute SLCA quickly in the node sets which is classified by using ELM. In this paper, we adopt ELM to classify nodes and compute probability. We propose two algorithms that are based on ELM and probability threshold to improve the overall performance. The experimental results verify the benefits of our methods according to various evaluation metrics.

Research on Basic Operations for Query Probabilistic XML Document Based on Path Set

Probabilistic XML tree is a typical probabilistic XML data model for representing uncertainty in the form of probability which can be described the nested probabilistic XML element unit. The path set in probabilistic XML unit tree can be analyzed through parsing it and drawing out the corresponding probabilistic XML unit schema tree. The node probability and the node probability threshold computation are defined first. Then the set operations are defined. The probabilistic XML XQuery sentences are extended to query a node or nodes probability, path probability and to list the possible world set. The instance analysis shows that probabilistic XML data is an effective method representing probability data and the given basic operation can realize the related probability query.

ELCA evaluation for keyword search on probabilistic XML data

As probabilistic data management is becoming one of the main research focuses and keyword search is turning into a more popular query means, it is natural to think how to support keyword queries on probabilistic XML data. With regards to keyword query on deterministic XML documents, ELCA (Exclusive Lowest Common Ancestor) semantics allows more relevant fragments rooted at the ELCAs to appear as results and is more popular compared with other keyword query result semantics (such as SLCAs). In this paper, we investigate how to evaluate ELCA results for keyword queries on probabilistic XML documents. After defining probabilistic ELCA semantics in terms of possible world semantics, we propose an approach to compute ELCA probabilities without generating possible worlds. Then we develop an efficient stack-based algorithm that can find all probabilistic ELCA results and their ELCA probabilities for a given keyword query on a probabilistic XML document. Finally, we experimentally evaluate the proposed ELCA algorithm and compare it with its SLCA counterpart in aspects of result probability, time and space efficiency, and scalability.

Efficient probabilistic XML query processing using an extended labeling scheme and a lightweight index

Recently there is a growing interest in the data model and query processing for probabilistic XML data. There are many potential applications of probabilistic data, and the XML data model is suitable to represent hierarchical information and data uncertainty of different levels naturally. However, the previously proposed probabilistic XML data models and query processing techniques separate finding data matches with evaluating the probabilities of results. Therefore, they should repeatedly access the data and need to get full data of paths given in queries to calculate the probabilities of results.In this paper, we propose an extended interval-based labeling scheme for the probabilistic XML data tree and an efficient query processing procedure using the labeling scheme. Against previous researches, our method accesses only the labels of data specified in queries and finds data matches simultaneously with evaluating the probability of each data match. Also, we present an extended probabilistic XML query model with the predicates for the values of probabilities and a lightweight index for those probabilities in order to eliminate unnecessary access to data that will not be included in results.Experimental results show that our approach is efficient in probabilistic XML query processing and our index scheme significantly improves the performance of query processing when the predicates for the values of probabilities are given.

Efficient processing of top-k twig queries over probabilistic XML data

The flexibility of XML data model allows a more natural representation of uncertain data compared with the relational model. Matching twig pattern against XML data is a fundamental problem in querying information from XML documents. For a probabilistic XML document, each twig answer has a probabilistic value because of the uncertainty of data. The twig answers that have small probabilistic value are useless to the users, and usually users only want to get the answers with the k largest probabilistic values. To this end, existing algorithms for ordinary XML documents cannot be directly applicable due to the need for handling probability distributional nodes and efficient calculation of top-k probabilities of answers in probabilistic XML. In this paper, we address the problem of finding twig answers with top-k probabilistic values against probabilistic XML documents directly. We propose a new encoding scheme called PEDewey for probabilistic XML in this paper. Based on this encoding scheme, we then design two algorithms for finding answers of top-k probabilities for twig queries. One is called ProTJFast, to process probabilistic XML data based on element streams in document order, and the other is called PTopKTwig, based on the element streams ordered by the path probability values. Experiments have been conducted to study the performance of these algorithms.

Research on Querying Node Probability Method in Probabilistic XML Data Based on Possible World

Research on Querying Node Probability Method in Probabilistic XML Data Based on Possible World

Modeling and querying probabilistic XML data

We survey recent results on modeling and querying probabilistic XML data. The literature contains a plethora of probabilistic XML models [2, 13, 14, 18, 21, 24, 27], and most of them can be represented by means of p-documents [18] that have, in addition to ordinary nodes, distributional nodes that specify the probabilistic process of generating a random document. The above models are families of p-documents that differ in the types of distributional nodes in use. The focus of this survey is on the tradeoff between the ability to express real-world probabilistic data (in particular, by taking correlations between atomic events into account) and the efficiency of query evaluation. We concentrate on two important issues. The first is the ability to efficiently translate a pdocument of one family into that of another. The second is the complexity of query evaluation over pdocuments (under the usual semantics of querying probabilistic data, e.g., [4, 9, 10]). It turns out that efficient evaluation of a large class of queries (i.e., twig patterns with projection and aggregate functions) is realizable in models where distributional nodes are probabilistically independent. In other models, the evaluation of a query with projection is very often intractable. In comparison, very simple conjunctive queries are intractable over probabilistic models of relational databases, even when the tuples are probabilistically independent [9, 10]. To handle the limitation exhibited by the above tradeoff, various approaches have been proposed. The first is to allow query answers to be approximate [18], which makes the evaluation of twig patterns with projection tractable in the most expressive family of p-documents, among those considered. This tractability, however, does not carry over to nonmonotonic queries, such as twig patterns with negation or aggregation. The approach presented in [7]

Probabilistic interval XML

Interest in XML databases has been expanding rapidly over the last few years. In this paper, we study the problem of incorporating probabilistic information into XML databases. We propose the Probabilistic Interval XML ( PIXML for short) data model in this paper. Using this data model, users can express probabilistic information within XML markups. In addition, we provide two alternative formal model-theoretic semantics for PIXML data. The first semantics is a “global” semantics which is relatively intuitive, but is not directly amenable to computation. The second semantics is a “local” semantics which supports efficient computation. We prove several correspondence results between the two semantics. To our knowledge, this is the first formal model theoretic semantics for probabilistic interval XML. We then provide an operational semantics that may be used to compute answers to queries and that is correct for a large class of probabilistic instances.